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Abstract #4614

Discriminative deep feature fusion of Contrast-enhanced MR for malignancy characterization of hepatocellular carcinoma

Wu Zhou1, Tianyou Dou1, Miaoyun Zhangwen1, Hui Ye1, Dong Cao1, Honglai Zhang1, Changhong Liang2, Hairong Zheng3, and Lijuan Zhang3

1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, 2Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, 3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

The malignancy of hepatocellular carcinoma (HCC) is of great significance to prognosis. Recently, deep feature in the arterial phase of Contrast-enhanced MR has been shown to be superior to texture features for malignancy characterization of HCCs. However, only arterial phase was used for deep feature extraction, ignoring the impact of other phases in Contrast-enhanced MR for malignancy characterization. In this work, we design a discriminative multimodal deep feature fusion framework to both extract correlation and separation of deep features between Contrast-enhanced MR images for malignancy characterization of HCC, which outperforms the simply concatenation and the recently proposed deep correlation model.

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